Language level support for shared virtual memory

ABSTRACT

Embodiments of the invention provide language support for CPU-GPU platforms. In one embodiment, code can be flexibly executed on both the CPU and GPU. CPU code can offload a kernel to the GPU. That kernel may in turn call preexisting libraries on the CPU, or make other calls into CPU functions. This allows an application to be built without requiring the entire call chain to be recompiled. Additionally, in one embodiment data may be shared seamlessly between CPU and GPU. This includes sharing objects that may have virtual functions. Embodiments thus ensure the right virtual function gets invoked on the CPU or the GPU if a virtual function is called by either the CPU or GPU.

RELATED APPLICATION

This application claims the benefit of provisional patent applicationNo. 61/199,095, filed on Nov. 13, 2008, entitled “Shared VirtualMemory.” This application is also related to U.S. patent applicationSer. No. 12/317,853, filed on Dec. 30, 2008, entitled “Shared VirtualMemory.”

BACKGROUND

This relates generally to shared virtual memory implementations and inparticular to fine-grain partitioning between a CPU and a GPU.

The computing industry is moving towards a heterogeneous platformarchitecture consisting of a general purpose CPU along with programmableGPUs attached both as a discrete or integrated device. These GPUs areconnected over both coherent and non-coherent interconnects, havedifferent industry standard architectures (ISAs) and may use their ownoperating systems.

Computing platforms composed of a combination of a general purposeprocessor (CPU) and a graphics processor (GPU) have become ubiquitous,especially in the client computing space. Today, almost all desktop andnotebook platforms ship with one or more CPUs along with an integratedor a discrete GPU. For example, some platforms have a processor pairedwith an integrated graphics chipset, while the remaining use a discretegraphics processor connected over an interface, such as PCI-Express.Some platforms ship as a combination of a CPU and a GPU. For example,some of these include a more integrated CPU-GPU platform while othersinclude a discrete graphics processor to complement integrated GPUofferings.

These CPU-GPU platforms may provide significant performance boost onnon-graphics workloads in image processing, medical imaging, datamining, and other domains. The massively data parallel GPU may be usedfor getting high throughput on the highly parallel portions of the code.

Existing language mechanisms for executing applications on a CPU-GPUplatform tend to only support an offload model in which a kernel(function) is offloaded to the GPU. The arguments to the function arecopied to the device. If the arguments include pointer-containing datastructures, then the arguments are marshaled and passed to the GPU.Similarly the return value is copied back to the CPU.

These existing models (also referred hereafter as the device models)have a number of disadvantages: 1) they prevent a natural partitioningof an application between the CPU and GPU. An application usually hassome throughput oriented parts and some scalar parts. For example a gameapplication will have rendering that is suited for the GPU, but willalso have physics and AI that is suited for the CPU. Current models tendto force most of the computation to be offloaded to the GPU.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a depiction of a CPU-GPU memory model in accordance with oneembodiment.

FIG. 2 is a flow chart for one embodiment of language constructs.

FIG. 3 is a flow chart for another embodiment of language constructs.

FIG. 4 is a flow chart of an embodiment for implementing remote calls.

FIG. 5 is a flow chart of an embodiment of a translation scheme.

FIG. 6 is a flow chart of another embodiment of a translation scheme.

FIG. 7 is a flow chart of an embodiment for function pointerinvocations.

DETAILED DESCRIPTION

Embodiments of the invention provide language support for CPU-GPUplatforms. In one embodiment, code can be flexibly executed on both theCPU and GPU. CPU code can offload a kernel to the GPU. That kernel mayin turn call preexisting libraries on the CPU, or make other calls intoCPU functions. This allows an application to be built without requiringthe entire call chain to be recompiled. Additionally, in one embodimentdata may be shared seamlessly between CPU and GPU. This includes sharingobjects that may have virtual functions. Embodiments thus ensure theright function gets invoked on the CPU or the GPU if a virtual functionis called on a shared object by either the CPU or GPU.

The GPGPU environment may be made more versatile by sharing memorybetween the CPU and GPU and seamless execution of code on a CPU-GPUplatform as described herein. For purposes of explanation, we willassume the existence of a keyword “shared” that may be used to denotevariables that are shared between the CPU and GPU—i.e. have the samevirtual address in both CPU and GPU.

In particular, embodiments of the invention provide a uniformprogramming model for both integrated and discrete devices. The modelalso works uniformly for multiple GPU cards and hybrid GPU systems(discrete and integrated). This allows software vendors to write asingle application stack and target it to all the different platforms.Additionally, embodiments of the invention provide a shared memory modelbetween the CPU and GPU. Instead of sharing the entire virtual addressspace, only a part of the virtual address space needs to be shared. Thisallows efficient implementation in both discrete and integratedsettings. Furthermore, language annotations may be used to demarcatecode that must run on the GPU, and also CPU code that can be invokedfrom the GPU. Language support may be extended to include features suchas function pointers.

Embodiments of the shared memory model provide a novel programmingparadigm. In particular, data structures may be seamlessly sharedbetween the CPU and GPU, and pointers may be passed from one side to theother without requiring any marshalling. For example, in one embodimenta game engine may includes physics, artificial intelligence (AI), andrendering. The physics and AI code may be best executed on the CPU,while the rendering may be best executed on the GPU. Data structures mayneed to be shared, such as the scene graph, between the CPU & GPU. Suchan execution model may not be possible in some current programmingenvironments since the scene graph would have to be serialized (ormarshaled) back and forth. However, in embodiments of the shared memorymodel, the scene graph may simply reside in shared memory and beaccessed both by the CPU and GPU.

In one embodiment, the full programming environment, including thelanguage and runtime support, is implemented. A number of highlyparallel non-graphics workloads may be ported to this environment. Theimplementation may work on heterogeneous operating systems, i.e. withdifferent operating systems running on the CPU and GPU. Moreover, userlevel communication may be allowed between the CPU and GPU. This maymake the application stack more efficient since the overhead of the OSdriver stack in CPU-GPU communication may be eliminated. The programmingenvironment may be ported to two different heterogeneous CPU-GPUplatform simulators—one simulates the GPU attached as a discrete deviceto the CPU, while the other simulates an integrated CPU-GPU platform.

In summary, embodiments of the programming model for CPU-GPU platformsmay:

-   -   Provide a uniform programming model for discrete, integrated,        multi-GPU cards and hybrid GPU configurations.    -   Provide shared memory semantics between the CPU and GPU allowing        pointers to be passed and data structures to be shared freely        between the CPU and GPU    -   Be implemented in a heterogeneous CPU-GPU platform with        different ISAs and different operating systems on the CPU and        GPU.    -   Enable user-level communication between the CPU and GPU thus        making the application stack much more efficient.        Memory Model

FIG. 1 is a depiction of a GPU-CPU memory model in accordance with oneembodiment. In one embodiment, the memory model 100 provides a window ofshared virtual addresses 130 between the CPU 110 and GPU 120, such as inpartitioned global address space (PGAS) languages. Any data structurethat is shared between the CPU 110 and GPU 120 typically must beallocated by the programmer in this space 130. The system may provide aspecial malloc function that allocates data in this space 130. Staticvariables may be annotated with a type quantifier to have them allocatedin the shared window 130. However, unlike PGAS languages there is nonotion of affinity in the shared window. This is because data in theshared space 130 migrates between the CPU and GPU caches as it gets usedby each processor. Also unlike PGAS implementations, the representationof pointers does not change between the shared and private spaces. Theremaining virtual address space is private to the CPU 110 and GPU 120.By default data gets allocated in this space, and is not visible to theother side. This partitioned address space approach may cut down on theamount of memory that needs to be kept coherent and enables a moreefficient implementation for discrete devices.

The embodiment of the memory model may be extended to multi-GPU andhybrid configurations. In particular, the window of shared virtualaddresses may be extended across all the devices. Any data structuresallocated in this shared address window 130 may be visible to all agentsand pointers in this space may be freely exchanged. In addition, everyagent has its own private memory.

Language Constructs

In one embodiment, to address platform heterogeneity, constructs may beadded to C/C++ that allow the programmer to specify whether a particulardata item should be shared or private, and to specify whether aparticular code chunk should be run on the CPU or GPU.

The first construct may be a shared type qualifier which specifies avariable that is shared between the CPU & GPU. The qualifier may also beassociated with pointer types to imply that the target of the pointer isin shared space. In one embodiment, this may be implemented as:

  shared int var1;         // int is in shared space   int var2;             // int is not in shared space   shared int* ptr1;        //ptr1 points to a shared location   int* ptr2;             // ptr2 pointsto private space   shared int *shared ptr1;  // ptr1 points to sharedand is shared

In one embodiment, the programmer tags all data that is shared betweenthe CPU and GPU with the shared keyword. The compiler allocates globalshared variables in the shared memory space, while the system provides aspecial malloc function to allocate data in the shared memory. Theactual virtual address range in each space may be decided by the systemand may be transparent to the user. Variables with automatic storage(e.g. stack allocated variables) are not allowed to be marked with thekeyword shared.

FIG. 2 is a flow chart for one embodiment of language constructs. Asequence 200 may be implemented in firmware, software, or hardware.Software embodiments may be stored on a computer-readable medium such asan optical disk, a magnetic disk, or a semiconductor memory. Anattribute, such as attribute (GPU), may be used to annotate functionsthat should be executed on the GPU (block 210). For such functions, thecompiler generates GPU-specific code (block 220). When a non-annotatedfunction calls a GPU annotated function, it implies a call from the CPUto GPU. The compiler checks that all pointer arguments have shared typeand invokes a runtime API for the remote call (block 230).

Function pointer types are also annotated with the attribute notationimplying that they point to functions that are executed on GPU. Nonannotated function pointer types point to functions that execute on theCPU. The compiler checks type equivalence during an assignment—forexample, a function pointer with the GPU attribute may be assigned theaddress of a GPU annotated function.

FIG. 3 is a flow chart for another embodiment of language constructs. Asequence 300 may be implemented in firmware, software, or hardware. Aconstruct denotes functions that execute on the CPU but may be calledfrom the GPU (block 310). These functions may be denoted using(_attribute(wrapper)). When a GPU function calls a wrapper function, thecompiler may invoke a runtime API for the remote call from the GPU tothe CPU (block 320). Making the GPU to CPU calls explicit may have theadvantage that the compiler checks that any pointer arguments have theshared type. Moreover, this may be also important to deal with OSheterogeneity.

Data Annotation Rules

In one embodiment, data annotation rules may be as follows:

1. Shared may be used to qualify the type of variables with globalstorage. Shared may not be used to qualify a variable with automaticstorage unless it qualifies a pointer's referenced type.

2. Pointer in private space may point to any space. Pointer in sharedspace may only point to shared space but not to private space.

The following rules may be applied to pointer manipulations:

-   -   1. Binary operator (+, −, ==, !=, >, <, >=, <= . . . ) is only        allowed between two pointers pointing to same space. When an        integer type expression is added to or subtracted from a        pointer, the result has the same type as the pointer.    -   2. Assignment/casting from pointer-to-shared to        pointer-to-private is allowed. If a type is not annotated assume        that it denotes a private object. This makes it difficult to        pass shared objects to legacy functions since their signature        requires private objects. The cast allows avoiding copying        between private and shared spaces when passing shared data to a        legacy function.    -   3. Assignment/casting from pointer-to-private to        pointer-to-shared is allowed only through a dynamic_cast. The        dynamic_cast checks at runtime that the pointer-to-shared        actually points to shared space. If the check fails, an error is        thrown and the user has to explicitly copy the data from private        space to shared space. With this capability, code may        efficiently get return value from legacy functions.

Embodiments of the language allow casting between the two spaces, withpossibly a dynamic check, since the data representation remains the sameregardless of whether the data is in shared or private space. Evenpointers may have the same representation regardless of whether they arepointing to private or shared space. Given any virtual address V in theshared address window, both CPU and GPU have their own local physicaladdress corresponding to this virtual address. Pointers on CPU and GPUread from this local copy of the address, and the local copies getsynced up as required by the memory model. This ability to cast pointershas been critical to porting workloads to the system since it allowseasy interoperability with legacy code.

Not qualifying single member of aggregate type:

Shared may not be used to qualify a single member of a structure orunion unless it qualifies a pointer's referenced type. A structure orunion type may have the shared qualifier which then requires all fieldsto have the shared qualifier as well.

Implementation

In one embodiment, two pragmas may be used to annotate functiondeclarations:

-   -   #pragma GPU may be used to annotate functions that can be        executed on the GPU. The compiler generates GPU code for all        such functions that are then loaded on the GPU.    -   #pragma wrapper may be used to annotate functions that are        executed on the CPU, but may be called from the GPU.

One embodiment of a compilation scheme may include as follows:

-   -   A #pragma GPU function called from a non-GPU function (ie non        annotated function) results in a call into GPU to execute the        function. The compiler inserts the appropriate runtime API call.    -   A #pragma GPU function is not allowed to call a non-annotated        function    -   A #pragma GPU function calling into a #pragma wrapper function        results in a call from the GPU to the CPU. The compiler inserts        the appropriate runtime API call.    -   A #pragma wrapper function is not allowed to call into a #pragma        GPU function.    -   Any pointer parameter to a GPU or wrapper function has the        shared type annotation.

The pragma declarations are part of the type of a function and hencealso accompany the type declaration for a function pointer. The compilerchecks at every function pointer assignment that the type of the rvalueis the same as the type of the lvalue (after factoring in the pragmadeclarations).

Embodiments of the invention may support calling preexisting binaries(from GPU) in the following way. Suppose the user wants to call theprecompiled library Foo(int arg) from a GPU function. The user simplyneeds to write a wrapper (say #pragma wrapper FooWrapper (shared intarg1)). Within this wrapper function it calls the original function Fooand passes it the argument arg1. The compiler will copy the argumentinto the shared area, and make a call from the GPU to the CPU)

  #pragma GPU imageKernel( ...) {     x = strlen(char* s);  // supposewe want to use a preexisting string library function in new GPU code   }  The user writes:   #pragma wrapper int strlenWrapper(shared char*str);   #pragma GPU imageKernel( ... ) {     arg = copyToShared(s);  //copies from private to shared space     x = strlenWrapper(arg);  //compiler typechecks and inserts the runtime API call for CPU code   }  // This code is part of the application running on the CPU   #pragmawrapper int strlenWrapper(shared char* s) {      return (strlen(s));   }

The main difficulty in implementing the above is that the GPU and CPUhave different address spaces and different linker and loader. Theapplication code may be loaded at different addresses in the CPU and GPUaddress domains. Hence when there is a function call from the CPU toGPU, unlike an ordinary function call, the compiled code on the CPU doesnot know the address of the target. For example, on a GPU function callfrom a non annotated function (i.e. calling a GPU function from a CPUfunction), the compiler/linker/loader on the CPU side does not know theaddress of the target on the GPU side. Hence it may be impossible forthe compiler to generate the proper call address.

Embodiments of the invention address this by creating a fat binary thatcontains both the GPU code and the CPU code. The binary is then loadedinto both the CPU and GPU spaces. As mentioned before, the functions maybe at different offsets in the two binaries since they may end up beingloaded at different addresses. Further when a GPU or wrapper function iscompiled, the name of the function is stored at a fixed offset from thebeginning of the function (for example just before the code for thefunction).

Both on the GPU and the CPU side a table of function names and addressesis maintained. When a remote call is made from one side to the other,instead of generating an address to call, the compiler sends the name ofthe function to call and a search is performed in the jump table. Whenthe application is loaded, the table is populated. For each #pragmawrapper function, the compiler generates a call into a registrationfunction on the CPU side. For each #pragma GPU function, the compilergenerates a call into the registration function on the GPU side. Theseregistration functions take the runtime address of the correspondingfunction and populate the table with the name and the address.

At a remote call, the name in the table is accessed, the correspondingaddress obtained and the dispatch performed.

In some embodiments, the above method may not work for function pointerssince the compiler can not associate a name with the function pointercall. All it has is a dynamic address. At runtime this address may beused to look up the name of the function (since the name is stored at afixed offset). The name can then be sent as part of the remote call, thetable lookup performed (as in the direct function call) and thendispatched to the function in question.

One embodiment of the pseudo code for the mechanism is shown below:

   Step 1: registration functions with <funcName, funcPointer>       Foreach #pragma GPU function        registerGPUFunc(funcName, funcPointer){       if GPU:        addToJumpTable(funcName, funcPointer);          else  //store in fixed offset, e.g. before the func code       storeFuncNameByFuncPointer(funcName,     funcPointer);       }   For each #pragma wrapper function:     registerWrapperFunc(funcName,funcPointer) {      if CPU:        addToJumpTable(funcName,funcPointer);        else  //store in fixed offset, e.g. before the    func code        storeFuncNameByFuncPointer(funcName,    funcPointer);     }    Step 2: transform remote call:     For eachGPU_function call in CPU side and wrapper_function call in GPU side:      remoteCallByName(funcName, funcParas) {         sendFuncNameToRemote(funcName, funcParas);       }    For eachGPU_function pointer call in CPU side and wrapper_function pointer callon GPU:       remoteCallByPointer(funPointer, funcParas) {         funcName =     getFuncNameByFuncPointer(funcPointer);         sendFuncNameToRemote(funcName, funcParas);       }    Step 3:Call the function when receiving a remote call request:    executeRemoteCall(funcName, funcParas) {     funcPointer =lookupJumpTable(funcName);     dispatchFunc(funcPointer, funcParas);    }Implementing Remote Calls

In one embodiment, a remote call from the CPU to GPU, or GPU to CPU maybe complicated by the fact that the two processors have differentoperating systems and different loaders. The two binaries are alsoloaded separately and asynchronously. Suppose that the CPU code makessome calls into the GPU. When the CPU binary is loaded, the GPU binaryhas still not been loaded and hence the addresses for GPU functions arestill not known. Therefore, the OS loader may not patch up thereferences to GPU functions in the CPU binary. Similarly, when the GPUbinary is being loaded, the GPU loader does not know the addresses ofany CPU functions being called from GPU code and hence may not patchthose addresses.

FIG. 4 is a flow chart of an embodiment for implementing remote calls. Asequence 400 may be implemented in firmware, software, or hardware. Inone embodiment, remote calls may be implemented by using a combinationof compiler and runtime techniques. The language rules ensure that anyfunction involved in remote calls (GPU or wrapper attribute functions)is annotated by the user. When compiling such functions, the compileradds a call to a runtime API that registers function addressesdynamically (block 410). The compiler creates an initialization functionfor each file that invokes all the different registration calls (block420). When the binary gets loaded, the initialization function in eachfile gets called (block 430). The shared address space contains a jumptable that is populated dynamically by the registration function (block440). The table contains one slot for every annotated function. Theformat of every slot is <funcName, funcAddr> where funcName is a literalstring of the function name and funcaddr is the runtime address of thefunction.

FIGS. 5 and 6 are flow charts of embodiments of a translation scheme.Sequences 500 and 600 may be implemented in firmware, software, orhardware. In accordance with one embodiment, the translation scheme maywork as follows.

1. If a GPU (CPU) function is being called within a GPU (CPU) function(block 510), the compiler generated code will do the call as is (block520).

2. If a GPU function is being called within a CPU function (block 610),the compiler generated code will do a remote call to GPU:

-   -   2.1. The compiler generated code will look up the jump table        with the function name and obtain the function address (block        620).    -   2.2. The generated code will pack the arguments into an argument        buffer in shared space (block 630). It will then call a dispatch        routine on the GPU side passing in the function address and the        argument buffer address (block 640).

There is similar process for a wrapper function except that it is aremote call to CPU if a wrapper function is called in a GPU function.

FIG. 7 is a flow chart of an embodiment for function pointerinvocations. A sequence 700 may be implemented in firmware, software, orhardware. For function pointer invocations, the translation scheme maywork as follows. When a function pointer with GPU annotation is assigned(block 710), the compiler generated code will look up the jump tablewith the function name and assign the function pointer with obtainedfunction address (block 720). Although the lookup may be optimized outwhen GPU annotated function pointer is assigned within GPU code, theoptimization may be forsaken to use a single strategy for all functionpointer assignments. If a GPU function pointer is being called within aGPU function (block 730), the compiler generated code will do the callas is (block 740). If a GPU function pointer is being called within aCPU function (block 730), the compiler generated code will do a remotecall to GPU side (block 750). The process is similar for a wrapperfunction pointer except that there is a remote call to CPU side ifwrapper function pointer is called in a GPU function.

The CPU-GPU signaling happens with task queues in the PCI aperturespace. Daemon threads on both sides poll their respective task queuesand when they find an entry in the task queue, they spawn a new threadto invoke the corresponding function. In one embodiment, the API forremote invocations is described below.

  /* remote calls. The function type and arg types encapsulate thefunction pointer and arguments.  */   RPCHandlercallRemote(myoFunctionType, MyoRPCArgType);   intresultReady(MyoRPCHandler);   MyoType getResult(MyoRPCHandler)Code Example

This section illustrates one embodiment of the proposed programmingmodel through a code example that illustrates a simple, vector addition(addTwoVectors) that may be accelerated through the GPU.

int addTwoVectors(int* a, int* b, int* c) {   for (i = 1 to 64) {   c[i] = a[i] + b[i]  } } int someApp( ...) {    int *a = malloc (..);int *b = malloc (..); int *c = malloc (..);    for (i = 1 to 64) {a[i] =; b[i] = ; c[i] = ;} // initialize    addTwoVectors(a, b, c);    ...   }   In the embodiment of the programming model, this would be written as:  _attribute(GPU) int addTwoVectors(shared int* a, shared int* b, sharedint* c)   {    for (i = 1 to 64) {     c[i] = a[i] + b[i];    }   }  int someApp(..)   {    shared int* a = sharedMalloc (..); //allocatein shared region    shared int* b = sharedMalloc (..); //allocate inshared region    shared int* c = sharedMalloc (..); //allocate in sharedregion    for (i = 1 to 64) {a[i] = ; b[i] = ; c[i] = ;} // initialize   addTwoVectors(a, b, c);    // compiler converts into remote call   ...   }

In the above implementation, arrays a, b, c are allocated in sharedspace by calling the special malloc function. The remote call(addTwoVectors) acts as the release/acquire point and causes the memoryregion to be synced up between CPU & GPU.

One embodiment of a corresponding CUDA code snippet is presented below.Note that the user has to explicitly allocate the CPU and GPU memoryspaces and copy the data from one side to the other. Note also that ifthese were more complex pointer containing data structures, a simplememcpy would not be sufficient to transfer the data from one side to theother. Instead, explicit marshalling would be needed.

  int someApp(..)   {     int* a = malloc (..); // allocate in CPUmemory     int* b = malloc (..); // allocate in CPU memory     int* c =malloc (..); // allocate in CPU memory     int *aD, *bD, *cD; // arraysfor the GPU devices     for (i = 1 to 64) {a[i] = ; b[i] = ; c[i] = ;}// initialize     cudaMalloc(aD);  // allocate space on GPU    cudaMalloc(bD);  // allocate space on GPU     cudaMalloc(cD);  //allocate space on GPU     cudaMemcpy(aD, a, ...,cudaMemcpyHostToDevice); // copy a     cudaMemcpy(bD, b, ...,cudaMemcpyHostToDevice); // copy b     addTwoVectors << ...  >> (..) //do the GPU computation     cudaMemcpy(c, cD, ...,cudeMemcpyDeviceToHost); // copy c     ...   }

Embodiments of the invention may be implemented in a processor-basedsystem that may include a general-purpose processor coupled to a chipsetin one embodiment. The chipset may be coupled to a system memory and agraphics processor. The graphics processor may be coupled to a framebuffer, in turn coupled to a display. In one embodiment, the embodimentsof the invention shown in FIGS. 1-7 may be implemented as softwarestored in a computer-readable medium, such as the system memory.However, embodiments of the present invention may be also implemented inhardware or firmware.

CONCLUSION

Embodiments of the programming model provide a shared memory modelincluding language constructs for CPU-GPU platforms which enablesfine-grain concurrency between the CPU and GPU. The uniform programmingmodel may be implemented for both discrete and integrated configurationsas well as for multi-GPU and hybrid configurations. User annotations maybe used to demarcate code for CPU and GPU execution. User levelcommunication may be provided between the CPU and GPU thus eliminatingthe overhead of OS driver calls. A full software stack may beimplemented for the programming model including compiler and runtimesupport.

References throughout this specification to “one embodiment” or “anembodiment” mean that a particular feature, structure, or characteristicdescribed in connection with the embodiment is included in at least oneimplementation encompassed within the present invention. Thus,appearances of the phrase “one embodiment” or “in an embodiment” are notnecessarily referring to the same embodiment. Furthermore, theparticular features, structures, or characteristics may be instituted inother suitable forms other than the particular embodiment illustratedand all such forms may be encompassed within the claims of the presentapplication.

While the present invention has been described with respect to a limitednumber of embodiments, those skilled in the art will appreciate numerousmodifications and variations therefrom. It is intended that the appendedclaims cover all such modifications and variations as fall within thetrue spirit and scope of this present invention.

1. A method providing: shared memory semantics between a centralprocessing unit (CPU) and a graphics processing unit (GPU) includingallowing pointers to be passed and data structures to be shared as isbetween the CPU and GPU; sharing only a portion of virtual address spacebetween the CPU and the GPU; if a GPU function is being called within aGPU function, performing the call as is; if a GPU function is beingcalled within a CPU function, performing a remote call to the GPU; if aCPU function is being called within a CPU function, performing the callas is; and if a CPU function is being called within a GPU function,performing a remote call to the CPU.
 2. The method claimed in claim 1,wherein code can be flexibly executed on both the CPU and GPU.
 3. Themethod claimed in claim 2, further comprising: offloading a kernel tothe GPU using CPU code; and using the kernel to call preexistinglibraries on the CPU, or make other calls into CPU functions.
 4. Themethod claimed in claim 1, including sharing objects having virtualfunctions such that a correct virtual function is invoked on the CPU orthe GPU in response to a virtual function being called on a sharedobject by either the CPU or GPU.
 5. The method claimed in claim 1,further comprising: identifying data that is shared between the CPU andGPU with a shared keyword; allocating global shared variables in ashared memory space; and providing a function to allocate data in theshared memory.
 6. The method claimed in claim 1, further comprisingusing an attribute to indicate functions that should be executed on theCPU or GPU.
 7. The method claimed in claim 1, further comprising: addinga call to a runtime application program interface (API) that registersfunction addresses dynamically; creating an initialization function foreach file that invokes different registration calls; when a binary getsloaded, calling the initialization function in each file; and populatingdynamically a jump table in the shared address space to contain functionaddresses.
 8. The method claimed in claim 1, further comprising: lookingup a jump table with a function name and obtaining a function addressassociated with the function name; packing in arguments into a buffer ina shared memory space; and calling a dispatch routine on the GPU sidepassing in the function address and the argument buffer address.
 9. Themethod claimed in claim 1, further comprising: when a function pointerwith GPU annotation is assigned, looking up a jump table with a functionname and assigning the function pointer with an obtained functionaddress.
 10. The method claimed in claim 9, wherein if a GPU functionpointer is being called within a GPU function, compiler generated codewill perform the call as is.
 11. The method claimed in claim 10, whereinif a GPU function pointer is being called within a CPU function, thecompiler generated code will do a remote call to GPU side.
 12. Anon-transitory computer readable medium storing instructions that, ifexecuted, enable a processor-based system to: share memory semanticsbetween a CPU and a GPU including allowing pointers to be passed anddata structures to be shared as is between the CPU and GPU; add a callto a runtime API that registers function addresses dynamically create aninitialization function for each file that invokes differentregistration calls; when a binary gets loaded, call the initializationfunction in each file; and populate dynamically a jump table in theshared address space to contain function addresses.
 13. The computerreadable medium claimed in claim 12, further storing instructions to:execute code on both the CPU and GPU.
 14. The non-transitory computerreadable medium claimed in 12, further storing instructions to: offloada kernel to the GPU using CPU code; and use the kernel to callpreexisting libraries on the CPU, or make other calls into CPUfunctions.
 15. The non-transitory computer readable medium claimed inclaim 12, further storing instructions to: share objects that havevirtual functions such that the correct virtual function is invoked onthe CPU or the GPU in response to a virtual function being called on ashared object by either the CPU or GPU.
 16. The non-transitory computerreadable medium claimed in claim 12, further storing instructions to:identify data that is shared between the CPU and GPU with a sharedkeyword; allocate global shared variables in a shared memory space; andprovide a function to allocate data in the shared memory.
 17. Thenon-transitory computer readable medium claimed in claim 12, furtherstoring instructions to: use an attribute to indicate functions thatshould be executed on the CPU or GPU.